DROPS

Document

A Logic Programming Approach to Reaction Systems

Authors: Moreno Falaschi and Giulia Palma

Published in: OASIcs, Volume 86, Recent Developments in the Design and Implementation of Programming Languages (2020)

Abstract

Reaction systems (RS) are a computational framework inspired by the functioning of living cells, suitable to model the main mechanisms of biochemical reactions. RS have shown to be useful also for computer science applications, e.g. to model circuits or transition systems. Since their introduction about 10 years ago, RS matured into a fruitful and dynamically evolving research area. They have become a popular novel model of interactive computation. RS can be seen as a rewriting system interacting with the environment represented by the context. RS pose some problems of implementation, as it is a relatively recent computation model, and several extensions of the basic model have been designed. In this paper we present some preliminary work on how to implement this formalism in a logic programming language (Prolog). To the best of our knowledge this is the first approach to RS in logic programming. Our prototypical implementation does not aim to be highly performing, but has the advantage of being high level and easily modifiable. So it is suitable as a rapid prototyping tool for implementing several extensions of reaction systems in the literature as well as new ones. We also make a preliminary implementation of a kind of memoization mechanism for stopping potentially infinite and repetitive computations. Then we show how to implement in our interpreter an extension of RS for modeling a nondeterministic context and interaction between components of a (biological) system. We then present an extension of the interpreter for implementing the recently introduced networks of RS.

Cite as

Moreno Falaschi and Giulia Palma. A Logic Programming Approach to Reaction Systems. In Recent Developments in the Design and Implementation of Programming Languages. Open Access Series in Informatics (OASIcs), Volume 86, pp. 6:1-6:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

Copy BibTex To Clipboard

@InProceedings{falaschi_et_al:OASIcs.Gabbrielli.6,
  author =	{Falaschi, Moreno and Palma, Giulia},
  title =	{{A Logic Programming Approach to Reaction Systems}},
  booktitle =	{Recent Developments in the Design and Implementation of Programming Languages},
  pages =	{6:1--6:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-171-9},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{86},
  editor =	{de Boer, Frank S. and Mauro, Jacopo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.Gabbrielli.6},
  URN =		{urn:nbn:de:0030-drops-132282},
  doi =		{10.4230/OASIcs.Gabbrielli.6},
  annote =	{Keywords: reaction systems, logic programming, non deterministic context}
}

Document

DOI: 10.4230/OASIcs.Gabbrielli.11

Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy

Authors: Moreno Falaschi, Catuscia Palamidessi, and Marco Romanelli

Published in: OASIcs, Volume 86, Recent Developments in the Design and Implementation of Programming Languages (2020)

Abstract

This paper shows how we can combine the power of machine learning with the flexibility of constraints. More specifically, we show how machine learning models can be represented by first-order logic theories, and how to derive these theories. The advantage of this representation is that it can be augmented with additional formulae, representing constraints of some kind on the data domain. For instance, new knowledge, or potential attackers, or fairness desiderata. We consider various kinds of learning algorithms (neural networks, k-nearest-neighbours, decision trees, support vector machines) and for each of them we show how to infer the FOL formulae. Then we focus on one particular application domain, namely the field of security and privacy. The idea is to represent the potentialities and goals of the attacker as a set of constraints, then use a constraint solver (more precisely, a solver modulo theories) to verify the satisfiability. If a solution exists, then it means that an attack is possible, otherwise, the system is safe. We show various examples from different areas of security and privacy; specifically, we consider a side-channel attack on a password checker, a malware attack on smart health systems, and a model-inversion attack on a neural network.

Cite as

Moreno Falaschi, Catuscia Palamidessi, and Marco Romanelli. Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy. In Recent Developments in the Design and Implementation of Programming Languages. Open Access Series in Informatics (OASIcs), Volume 86, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

Copy BibTex To Clipboard

@InProceedings{falaschi_et_al:OASIcs.Gabbrielli.11,
  author =	{Falaschi, Moreno and Palamidessi, Catuscia and Romanelli, Marco},
  title =	{{Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy}},
  booktitle =	{Recent Developments in the Design and Implementation of Programming Languages},
  pages =	{11:1--11:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-171-9},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{86},
  editor =	{de Boer, Frank S. and Mauro, Jacopo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Gabbrielli.11},
  URN =		{urn:nbn:de:0030-drops-132338},
  doi =		{10.4230/OASIcs.Gabbrielli.11},
  annote =	{Keywords: Constraints, machine learning, privacy, security}
}

Search Results

Documents authored by Falaschi, Moreno

A Logic Programming Approach to Reaction Systems

Abstract

Cite as

Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy

Abstract

Cite as

Thanks for your feedback!

Could not send message